{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 7: ARGA & ARVGA  \n",
    "\n",
    "Paper:\n",
    "* [Adversarially Regularized Graph Autoencoder for Graph Embedding](https://www.ijcai.org/Proceedings/2018/0362.pdf)  \n",
    "\n",
    "Code:\n",
    " * [ARGA & ARVGA](https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/models/autoencoder.html)\n",
    " * [Example on clustering](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/argva_node_clustering.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/antonio/anaconda3/envs/geometric_new/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.11.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "os.environ['TORCH'] = torch.__version__\n",
    "print(torch.__version__)\n",
    "\n",
    "!pip install -q torch-scatter -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
    "!pip install -q torch-sparse -f https://data.pyg.org/whl/torch-${TORCH}.html\n",
    "!pip install -q git+https://github.com/pyg-team/pytorch_geometric.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path as osp\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics.cluster import (v_measure_score, homogeneity_score, completeness_score)\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import torch_geometric.transforms as T\n",
    "from torch_geometric.datasets import Planetoid\n",
    "from torch_geometric.nn import GCNConv\n",
    "from torch_geometric.nn.models.autoencoder import ARGVA\n",
    "from torch_geometric.utils import train_test_split_edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_cuda = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.x\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.tx\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.allx\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.y\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ty\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.ally\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.graph\n",
      "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.cora.test.index\n",
      "Processing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "dataset = 'Cora'\n",
    "path = osp.join('.', 'data', dataset)\n",
    "dataset = Planetoid(path, dataset, transform=T.NormalizeFeatures())\n",
    "data = dataset.get(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the number of nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_nodes = data.x.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the train/val/test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/antonio/anaconda3/envs/geometric_new/lib/python3.9/site-packages/torch_geometric/deprecation.py:12: UserWarning: 'train_test_split_edges' is deprecated, use 'transforms.RandomLinkSplit' instead\n",
      "  warnings.warn(out)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Data(x=[2708, 1433], y=[2708], val_pos_edge_index=[2, 263], test_pos_edge_index=[2, 527], train_pos_edge_index=[2, 8976], train_neg_adj_mask=[2708, 2708], val_neg_edge_index=[2, 263], test_neg_edge_index=[2, 527])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.train_mask = data.val_mask = data.test_mask = None\n",
    "data = train_test_split_edges(data)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the encoder classes (the same as in `Tutorial 6`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VEncoder(torch.nn.Module):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "        super(VEncoder, self).__init__()\n",
    "        self.conv1 = GCNConv(in_channels, 2 * out_channels, cached=True)\n",
    "        self.conv_mu = GCNConv(2 * out_channels, out_channels, cached=True)\n",
    "        self.conv_logstd = GCNConv(2 * out_channels, out_channels, cached=True)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        x = F.relu(self.conv1(x, edge_index))\n",
    "        return self.conv_mu(x, edge_index), self.conv_logstd(x, edge_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the discriminator class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(torch.nn.Module):\n",
    "    def __init__(self, in_channels, hidden_channels, out_channels):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.lin1 = torch.nn.Linear(in_channels, hidden_channels)\n",
    "        self.lin2 = torch.nn.Linear(hidden_channels, hidden_channels)\n",
    "        self.lin3 = torch.nn.Linear(hidden_channels, out_channels)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.lin1(x))\n",
    "        x = F.relu(self.lin2(x))\n",
    "        x = self.lin3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the training algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train():\n",
    "    model.train()\n",
    "    encoder_optimizer.zero_grad()\n",
    "    \n",
    "    z = model.encode(data.x, data.train_pos_edge_index)\n",
    "\n",
    "    for i in range(5):\n",
    "        idx = range(num_nodes)  \n",
    "        discriminator.train()\n",
    "        discriminator_optimizer.zero_grad()\n",
    "        discriminator_loss = model.discriminator_loss(z[idx]) # Comment\n",
    "        discriminator_loss.backward(retain_graph=True)\n",
    "        discriminator_optimizer.step()\n",
    " \n",
    "    loss = 0\n",
    "    loss = loss + model.reg_loss(z)  # Comment\n",
    "    \n",
    "    loss = loss + model.recon_loss(z, data.train_pos_edge_index)\n",
    "    loss = loss + (1 / data.num_nodes) * model.kl_loss()\n",
    "    loss.backward()\n",
    "\n",
    "    encoder_optimizer.step()\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a training test method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def test():\n",
    "    model.eval()\n",
    "    z = model.encode(data.x, data.train_pos_edge_index)\n",
    "\n",
    "    # Cluster embedded values using k-means.\n",
    "    kmeans_input = z.cpu().numpy()\n",
    "    kmeans = KMeans(n_clusters=7, random_state=0).fit(kmeans_input)\n",
    "    pred = kmeans.predict(kmeans_input)\n",
    "\n",
    "    labels = data.y.cpu().numpy()\n",
    "    completeness = completeness_score(labels, pred)\n",
    "    hm = homogeneity_score(labels, pred)\n",
    "    nmi = v_measure_score(labels, pred)\n",
    "\n",
    "    auc, ap = model.test(z, data.test_pos_edge_index, data.test_neg_edge_index)\n",
    "\n",
    "    return auc, ap, completeness, hm, nmi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize an encoder and a discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_size = 32\n",
    "encoder = VEncoder(data.num_features, out_channels=latent_size)\n",
    "\n",
    "discriminator = Discriminator(in_channels=latent_size, hidden_channels=64, \n",
    "                              out_channels=1) # Comment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the model and move everything to the GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ARGVA(encoder, discriminator)\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() and use_cuda else 'cpu')\n",
    "model, data = model.to(device), data.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator_optimizer = torch.optim.Adam(discriminator.parameters(), lr=0.001)\n",
    "encoder_optimizer = torch.optim.Adam(encoder.parameters(), lr=0.005)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001, Loss: 5.462, AUC: 0.710, AP: 0.739, Completeness: 0.118, Homogeneity: 0.118, NMI: 0.118\n",
      "Epoch: 002, Loss: 4.772, AUC: 0.680, AP: 0.714, Completeness: 0.091, Homogeneity: 0.090, NMI: 0.091\n",
      "Epoch: 003, Loss: 4.237, AUC: 0.668, AP: 0.704, Completeness: 0.074, Homogeneity: 0.071, NMI: 0.072\n",
      "Epoch: 004, Loss: 3.706, AUC: 0.665, AP: 0.700, Completeness: 0.079, Homogeneity: 0.075, NMI: 0.077\n",
      "Epoch: 005, Loss: 3.361, AUC: 0.667, AP: 0.700, Completeness: 0.071, Homogeneity: 0.068, NMI: 0.069\n",
      "Epoch: 006, Loss: 3.041, AUC: 0.674, AP: 0.703, Completeness: 0.083, Homogeneity: 0.078, NMI: 0.081\n",
      "Epoch: 007, Loss: 2.740, AUC: 0.689, AP: 0.712, Completeness: 0.103, Homogeneity: 0.102, NMI: 0.102\n",
      "Epoch: 008, Loss: 2.487, AUC: 0.706, AP: 0.725, Completeness: 0.130, Homogeneity: 0.132, NMI: 0.131\n",
      "Epoch: 009, Loss: 2.323, AUC: 0.714, AP: 0.733, Completeness: 0.139, Homogeneity: 0.142, NMI: 0.140\n",
      "Epoch: 010, Loss: 2.177, AUC: 0.722, AP: 0.743, Completeness: 0.149, Homogeneity: 0.150, NMI: 0.150\n",
      "Epoch: 011, Loss: 2.119, AUC: 0.724, AP: 0.747, Completeness: 0.164, Homogeneity: 0.163, NMI: 0.163\n",
      "Epoch: 012, Loss: 2.161, AUC: 0.719, AP: 0.745, Completeness: 0.174, Homogeneity: 0.172, NMI: 0.173\n",
      "Epoch: 013, Loss: 2.262, AUC: 0.721, AP: 0.747, Completeness: 0.185, Homogeneity: 0.186, NMI: 0.185\n",
      "Epoch: 014, Loss: 2.326, AUC: 0.727, AP: 0.753, Completeness: 0.199, Homogeneity: 0.200, NMI: 0.199\n",
      "Epoch: 015, Loss: 2.345, AUC: 0.735, AP: 0.758, Completeness: 0.221, Homogeneity: 0.224, NMI: 0.223\n",
      "Epoch: 016, Loss: 2.342, AUC: 0.741, AP: 0.762, Completeness: 0.221, Homogeneity: 0.222, NMI: 0.221\n",
      "Epoch: 017, Loss: 2.359, AUC: 0.750, AP: 0.769, Completeness: 0.227, Homogeneity: 0.230, NMI: 0.228\n",
      "Epoch: 018, Loss: 2.417, AUC: 0.762, AP: 0.779, Completeness: 0.234, Homogeneity: 0.235, NMI: 0.235\n",
      "Epoch: 019, Loss: 2.448, AUC: 0.773, AP: 0.787, Completeness: 0.243, Homogeneity: 0.248, NMI: 0.245\n",
      "Epoch: 020, Loss: 2.510, AUC: 0.777, AP: 0.789, Completeness: 0.289, Homogeneity: 0.291, NMI: 0.290\n",
      "Epoch: 021, Loss: 2.603, AUC: 0.773, AP: 0.785, Completeness: 0.304, Homogeneity: 0.314, NMI: 0.309\n",
      "Epoch: 022, Loss: 2.726, AUC: 0.770, AP: 0.780, Completeness: 0.295, Homogeneity: 0.306, NMI: 0.300\n",
      "Epoch: 023, Loss: 2.945, AUC: 0.769, AP: 0.778, Completeness: 0.296, Homogeneity: 0.307, NMI: 0.302\n",
      "Epoch: 024, Loss: 3.170, AUC: 0.773, AP: 0.779, Completeness: 0.305, Homogeneity: 0.318, NMI: 0.311\n",
      "Epoch: 025, Loss: 3.335, AUC: 0.780, AP: 0.783, Completeness: 0.316, Homogeneity: 0.327, NMI: 0.322\n",
      "Epoch: 026, Loss: 3.439, AUC: 0.789, AP: 0.790, Completeness: 0.350, Homogeneity: 0.361, NMI: 0.355\n",
      "Epoch: 027, Loss: 3.506, AUC: 0.797, AP: 0.799, Completeness: 0.346, Homogeneity: 0.362, NMI: 0.354\n",
      "Epoch: 028, Loss: 3.528, AUC: 0.799, AP: 0.803, Completeness: 0.368, Homogeneity: 0.376, NMI: 0.372\n",
      "Epoch: 029, Loss: 3.577, AUC: 0.797, AP: 0.801, Completeness: 0.356, Homogeneity: 0.367, NMI: 0.361\n",
      "Epoch: 030, Loss: 3.713, AUC: 0.792, AP: 0.795, Completeness: 0.353, Homogeneity: 0.365, NMI: 0.359\n",
      "Epoch: 031, Loss: 3.839, AUC: 0.788, AP: 0.791, Completeness: 0.356, Homogeneity: 0.368, NMI: 0.362\n",
      "Epoch: 032, Loss: 4.028, AUC: 0.789, AP: 0.790, Completeness: 0.356, Homogeneity: 0.357, NMI: 0.357\n",
      "Epoch: 033, Loss: 4.102, AUC: 0.794, AP: 0.794, Completeness: 0.364, Homogeneity: 0.366, NMI: 0.365\n",
      "Epoch: 034, Loss: 4.186, AUC: 0.800, AP: 0.798, Completeness: 0.367, Homogeneity: 0.368, NMI: 0.367\n",
      "Epoch: 035, Loss: 4.184, AUC: 0.806, AP: 0.803, Completeness: 0.373, Homogeneity: 0.372, NMI: 0.373\n",
      "Epoch: 036, Loss: 4.362, AUC: 0.809, AP: 0.806, Completeness: 0.370, Homogeneity: 0.367, NMI: 0.368\n",
      "Epoch: 037, Loss: 4.382, AUC: 0.805, AP: 0.804, Completeness: 0.370, Homogeneity: 0.367, NMI: 0.369\n",
      "Epoch: 038, Loss: 4.352, AUC: 0.795, AP: 0.797, Completeness: 0.370, Homogeneity: 0.365, NMI: 0.367\n",
      "Epoch: 039, Loss: 4.439, AUC: 0.781, AP: 0.785, Completeness: 0.362, Homogeneity: 0.353, NMI: 0.358\n",
      "Epoch: 040, Loss: 4.455, AUC: 0.769, AP: 0.773, Completeness: 0.357, Homogeneity: 0.345, NMI: 0.351\n",
      "Epoch: 041, Loss: 4.379, AUC: 0.760, AP: 0.763, Completeness: 0.349, Homogeneity: 0.343, NMI: 0.346\n",
      "Epoch: 042, Loss: 4.435, AUC: 0.756, AP: 0.757, Completeness: 0.349, Homogeneity: 0.344, NMI: 0.347\n",
      "Epoch: 043, Loss: 4.348, AUC: 0.759, AP: 0.756, Completeness: 0.355, Homogeneity: 0.346, NMI: 0.350\n",
      "Epoch: 044, Loss: 4.384, AUC: 0.767, AP: 0.759, Completeness: 0.359, Homogeneity: 0.349, NMI: 0.354\n",
      "Epoch: 045, Loss: 4.351, AUC: 0.779, AP: 0.766, Completeness: 0.361, Homogeneity: 0.351, NMI: 0.356\n",
      "Epoch: 046, Loss: 4.297, AUC: 0.793, AP: 0.776, Completeness: 0.364, Homogeneity: 0.353, NMI: 0.358\n",
      "Epoch: 047, Loss: 4.286, AUC: 0.807, AP: 0.788, Completeness: 0.364, Homogeneity: 0.354, NMI: 0.359\n",
      "Epoch: 048, Loss: 4.194, AUC: 0.819, AP: 0.800, Completeness: 0.384, Homogeneity: 0.380, NMI: 0.382\n",
      "Epoch: 049, Loss: 4.118, AUC: 0.829, AP: 0.812, Completeness: 0.386, Homogeneity: 0.383, NMI: 0.385\n",
      "Epoch: 050, Loss: 4.119, AUC: 0.836, AP: 0.821, Completeness: 0.398, Homogeneity: 0.403, NMI: 0.400\n",
      "Epoch: 051, Loss: 4.046, AUC: 0.840, AP: 0.827, Completeness: 0.397, Homogeneity: 0.402, NMI: 0.399\n",
      "Epoch: 052, Loss: 3.925, AUC: 0.842, AP: 0.829, Completeness: 0.394, Homogeneity: 0.401, NMI: 0.398\n",
      "Epoch: 053, Loss: 3.887, AUC: 0.842, AP: 0.829, Completeness: 0.399, Homogeneity: 0.406, NMI: 0.403\n",
      "Epoch: 054, Loss: 3.823, AUC: 0.842, AP: 0.829, Completeness: 0.400, Homogeneity: 0.409, NMI: 0.404\n",
      "Epoch: 055, Loss: 3.823, AUC: 0.841, AP: 0.829, Completeness: 0.400, Homogeneity: 0.410, NMI: 0.405\n",
      "Epoch: 056, Loss: 3.803, AUC: 0.840, AP: 0.828, Completeness: 0.403, Homogeneity: 0.413, NMI: 0.408\n",
      "Epoch: 057, Loss: 3.775, AUC: 0.838, AP: 0.828, Completeness: 0.401, Homogeneity: 0.413, NMI: 0.407\n",
      "Epoch: 058, Loss: 3.802, AUC: 0.838, AP: 0.827, Completeness: 0.402, Homogeneity: 0.415, NMI: 0.408\n",
      "Epoch: 059, Loss: 3.834, AUC: 0.839, AP: 0.829, Completeness: 0.401, Homogeneity: 0.414, NMI: 0.408\n",
      "Epoch: 060, Loss: 3.866, AUC: 0.842, AP: 0.832, Completeness: 0.401, Homogeneity: 0.413, NMI: 0.407\n",
      "Epoch: 061, Loss: 3.926, AUC: 0.845, AP: 0.835, Completeness: 0.398, Homogeneity: 0.411, NMI: 0.404\n",
      "Epoch: 062, Loss: 3.963, AUC: 0.848, AP: 0.839, Completeness: 0.398, Homogeneity: 0.411, NMI: 0.405\n",
      "Epoch: 063, Loss: 4.018, AUC: 0.851, AP: 0.841, Completeness: 0.398, Homogeneity: 0.410, NMI: 0.404\n",
      "Epoch: 064, Loss: 4.084, AUC: 0.854, AP: 0.844, Completeness: 0.401, Homogeneity: 0.417, NMI: 0.409\n",
      "Epoch: 065, Loss: 4.106, AUC: 0.856, AP: 0.845, Completeness: 0.402, Homogeneity: 0.419, NMI: 0.410\n",
      "Epoch: 066, Loss: 4.182, AUC: 0.858, AP: 0.846, Completeness: 0.432, Homogeneity: 0.433, NMI: 0.433\n",
      "Epoch: 067, Loss: 4.281, AUC: 0.859, AP: 0.846, Completeness: 0.435, Homogeneity: 0.436, NMI: 0.436\n",
      "Epoch: 068, Loss: 4.233, AUC: 0.860, AP: 0.846, Completeness: 0.431, Homogeneity: 0.427, NMI: 0.429\n",
      "Epoch: 069, Loss: 4.184, AUC: 0.861, AP: 0.845, Completeness: 0.431, Homogeneity: 0.433, NMI: 0.432\n",
      "Epoch: 070, Loss: 4.249, AUC: 0.862, AP: 0.846, Completeness: 0.431, Homogeneity: 0.433, NMI: 0.432\n",
      "Epoch: 071, Loss: 4.236, AUC: 0.863, AP: 0.849, Completeness: 0.430, Homogeneity: 0.432, NMI: 0.431\n",
      "Epoch: 072, Loss: 4.280, AUC: 0.865, AP: 0.852, Completeness: 0.432, Homogeneity: 0.434, NMI: 0.433\n",
      "Epoch: 073, Loss: 4.228, AUC: 0.867, AP: 0.855, Completeness: 0.435, Homogeneity: 0.437, NMI: 0.436\n",
      "Epoch: 074, Loss: 4.325, AUC: 0.868, AP: 0.857, Completeness: 0.437, Homogeneity: 0.440, NMI: 0.439\n",
      "Epoch: 075, Loss: 4.180, AUC: 0.870, AP: 0.860, Completeness: 0.437, Homogeneity: 0.440, NMI: 0.439\n",
      "Epoch: 076, Loss: 4.162, AUC: 0.871, AP: 0.862, Completeness: 0.435, Homogeneity: 0.439, NMI: 0.437\n",
      "Epoch: 077, Loss: 4.151, AUC: 0.872, AP: 0.864, Completeness: 0.437, Homogeneity: 0.442, NMI: 0.439\n",
      "Epoch: 078, Loss: 4.121, AUC: 0.872, AP: 0.864, Completeness: 0.436, Homogeneity: 0.442, NMI: 0.439\n",
      "Epoch: 079, Loss: 4.012, AUC: 0.872, AP: 0.864, Completeness: 0.435, Homogeneity: 0.441, NMI: 0.438\n",
      "Epoch: 080, Loss: 4.103, AUC: 0.873, AP: 0.864, Completeness: 0.437, Homogeneity: 0.442, NMI: 0.439\n",
      "Epoch: 081, Loss: 3.958, AUC: 0.873, AP: 0.864, Completeness: 0.437, Homogeneity: 0.443, NMI: 0.440\n",
      "Epoch: 082, Loss: 3.985, AUC: 0.874, AP: 0.863, Completeness: 0.440, Homogeneity: 0.446, NMI: 0.443\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 083, Loss: 4.044, AUC: 0.876, AP: 0.864, Completeness: 0.440, Homogeneity: 0.446, NMI: 0.443\n",
      "Epoch: 084, Loss: 4.002, AUC: 0.878, AP: 0.866, Completeness: 0.443, Homogeneity: 0.449, NMI: 0.446\n",
      "Epoch: 085, Loss: 3.997, AUC: 0.880, AP: 0.868, Completeness: 0.445, Homogeneity: 0.451, NMI: 0.448\n",
      "Epoch: 086, Loss: 3.912, AUC: 0.882, AP: 0.871, Completeness: 0.450, Homogeneity: 0.456, NMI: 0.453\n",
      "Epoch: 087, Loss: 3.941, AUC: 0.885, AP: 0.874, Completeness: 0.455, Homogeneity: 0.459, NMI: 0.457\n",
      "Epoch: 088, Loss: 3.959, AUC: 0.886, AP: 0.876, Completeness: 0.462, Homogeneity: 0.467, NMI: 0.464\n",
      "Epoch: 089, Loss: 4.057, AUC: 0.888, AP: 0.879, Completeness: 0.465, Homogeneity: 0.470, NMI: 0.467\n",
      "Epoch: 090, Loss: 3.970, AUC: 0.889, AP: 0.881, Completeness: 0.464, Homogeneity: 0.469, NMI: 0.466\n",
      "Epoch: 091, Loss: 4.002, AUC: 0.890, AP: 0.882, Completeness: 0.465, Homogeneity: 0.469, NMI: 0.467\n",
      "Epoch: 092, Loss: 3.970, AUC: 0.891, AP: 0.883, Completeness: 0.455, Homogeneity: 0.459, NMI: 0.457\n",
      "Epoch: 093, Loss: 3.931, AUC: 0.892, AP: 0.884, Completeness: 0.456, Homogeneity: 0.459, NMI: 0.457\n",
      "Epoch: 094, Loss: 3.932, AUC: 0.892, AP: 0.885, Completeness: 0.449, Homogeneity: 0.453, NMI: 0.451\n",
      "Epoch: 095, Loss: 3.903, AUC: 0.893, AP: 0.886, Completeness: 0.450, Homogeneity: 0.454, NMI: 0.452\n",
      "Epoch: 096, Loss: 3.911, AUC: 0.893, AP: 0.886, Completeness: 0.450, Homogeneity: 0.453, NMI: 0.451\n",
      "Epoch: 097, Loss: 3.888, AUC: 0.893, AP: 0.886, Completeness: 0.448, Homogeneity: 0.451, NMI: 0.450\n",
      "Epoch: 098, Loss: 3.849, AUC: 0.893, AP: 0.886, Completeness: 0.448, Homogeneity: 0.451, NMI: 0.449\n",
      "Epoch: 099, Loss: 3.840, AUC: 0.892, AP: 0.886, Completeness: 0.448, Homogeneity: 0.451, NMI: 0.450\n",
      "Epoch: 100, Loss: 3.876, AUC: 0.892, AP: 0.886, Completeness: 0.449, Homogeneity: 0.454, NMI: 0.451\n",
      "Epoch: 101, Loss: 3.854, AUC: 0.892, AP: 0.887, Completeness: 0.449, Homogeneity: 0.454, NMI: 0.451\n",
      "Epoch: 102, Loss: 3.863, AUC: 0.894, AP: 0.888, Completeness: 0.451, Homogeneity: 0.457, NMI: 0.454\n",
      "Epoch: 103, Loss: 3.878, AUC: 0.895, AP: 0.890, Completeness: 0.453, Homogeneity: 0.460, NMI: 0.456\n",
      "Epoch: 104, Loss: 3.771, AUC: 0.896, AP: 0.892, Completeness: 0.454, Homogeneity: 0.461, NMI: 0.457\n",
      "Epoch: 105, Loss: 3.785, AUC: 0.897, AP: 0.893, Completeness: 0.490, Homogeneity: 0.492, NMI: 0.491\n",
      "Epoch: 106, Loss: 3.860, AUC: 0.898, AP: 0.894, Completeness: 0.490, Homogeneity: 0.493, NMI: 0.491\n",
      "Epoch: 107, Loss: 3.827, AUC: 0.899, AP: 0.895, Completeness: 0.488, Homogeneity: 0.490, NMI: 0.489\n",
      "Epoch: 108, Loss: 3.753, AUC: 0.900, AP: 0.896, Completeness: 0.484, Homogeneity: 0.487, NMI: 0.485\n",
      "Epoch: 109, Loss: 3.789, AUC: 0.901, AP: 0.897, Completeness: 0.485, Homogeneity: 0.489, NMI: 0.487\n",
      "Epoch: 110, Loss: 3.796, AUC: 0.902, AP: 0.898, Completeness: 0.485, Homogeneity: 0.488, NMI: 0.486\n",
      "Epoch: 111, Loss: 3.793, AUC: 0.902, AP: 0.898, Completeness: 0.485, Homogeneity: 0.489, NMI: 0.487\n",
      "Epoch: 112, Loss: 3.752, AUC: 0.902, AP: 0.898, Completeness: 0.486, Homogeneity: 0.490, NMI: 0.488\n",
      "Epoch: 113, Loss: 3.803, AUC: 0.902, AP: 0.899, Completeness: 0.487, Homogeneity: 0.492, NMI: 0.490\n",
      "Epoch: 114, Loss: 3.808, AUC: 0.902, AP: 0.899, Completeness: 0.486, Homogeneity: 0.491, NMI: 0.488\n",
      "Epoch: 115, Loss: 3.888, AUC: 0.902, AP: 0.900, Completeness: 0.487, Homogeneity: 0.493, NMI: 0.490\n",
      "Epoch: 116, Loss: 3.777, AUC: 0.903, AP: 0.901, Completeness: 0.489, Homogeneity: 0.495, NMI: 0.492\n",
      "Epoch: 117, Loss: 3.887, AUC: 0.904, AP: 0.902, Completeness: 0.486, Homogeneity: 0.494, NMI: 0.490\n",
      "Epoch: 118, Loss: 3.879, AUC: 0.905, AP: 0.904, Completeness: 0.490, Homogeneity: 0.496, NMI: 0.493\n",
      "Epoch: 119, Loss: 3.847, AUC: 0.907, AP: 0.905, Completeness: 0.487, Homogeneity: 0.494, NMI: 0.491\n",
      "Epoch: 120, Loss: 3.774, AUC: 0.908, AP: 0.906, Completeness: 0.489, Homogeneity: 0.497, NMI: 0.493\n",
      "Epoch: 121, Loss: 3.821, AUC: 0.909, AP: 0.908, Completeness: 0.489, Homogeneity: 0.496, NMI: 0.493\n",
      "Epoch: 122, Loss: 3.764, AUC: 0.910, AP: 0.909, Completeness: 0.490, Homogeneity: 0.498, NMI: 0.494\n",
      "Epoch: 123, Loss: 3.820, AUC: 0.910, AP: 0.910, Completeness: 0.491, Homogeneity: 0.498, NMI: 0.494\n",
      "Epoch: 124, Loss: 3.842, AUC: 0.911, AP: 0.911, Completeness: 0.491, Homogeneity: 0.498, NMI: 0.494\n",
      "Epoch: 125, Loss: 3.864, AUC: 0.911, AP: 0.911, Completeness: 0.489, Homogeneity: 0.497, NMI: 0.493\n",
      "Epoch: 126, Loss: 3.819, AUC: 0.912, AP: 0.912, Completeness: 0.490, Homogeneity: 0.498, NMI: 0.494\n",
      "Epoch: 127, Loss: 3.793, AUC: 0.912, AP: 0.912, Completeness: 0.490, Homogeneity: 0.498, NMI: 0.494\n",
      "Epoch: 128, Loss: 3.811, AUC: 0.913, AP: 0.913, Completeness: 0.490, Homogeneity: 0.498, NMI: 0.494\n",
      "Epoch: 129, Loss: 3.825, AUC: 0.913, AP: 0.913, Completeness: 0.490, Homogeneity: 0.499, NMI: 0.495\n",
      "Epoch: 130, Loss: 3.804, AUC: 0.913, AP: 0.913, Completeness: 0.488, Homogeneity: 0.496, NMI: 0.492\n",
      "Epoch: 131, Loss: 3.792, AUC: 0.914, AP: 0.913, Completeness: 0.489, Homogeneity: 0.497, NMI: 0.493\n",
      "Epoch: 132, Loss: 3.856, AUC: 0.913, AP: 0.912, Completeness: 0.488, Homogeneity: 0.497, NMI: 0.492\n",
      "Epoch: 133, Loss: 3.685, AUC: 0.913, AP: 0.912, Completeness: 0.491, Homogeneity: 0.500, NMI: 0.496\n",
      "Epoch: 134, Loss: 3.787, AUC: 0.913, AP: 0.911, Completeness: 0.494, Homogeneity: 0.503, NMI: 0.498\n",
      "Epoch: 135, Loss: 3.690, AUC: 0.912, AP: 0.910, Completeness: 0.490, Homogeneity: 0.500, NMI: 0.495\n",
      "Epoch: 136, Loss: 3.698, AUC: 0.912, AP: 0.909, Completeness: 0.489, Homogeneity: 0.500, NMI: 0.495\n",
      "Epoch: 137, Loss: 3.740, AUC: 0.912, AP: 0.909, Completeness: 0.491, Homogeneity: 0.502, NMI: 0.496\n",
      "Epoch: 138, Loss: 3.687, AUC: 0.912, AP: 0.909, Completeness: 0.490, Homogeneity: 0.501, NMI: 0.496\n",
      "Epoch: 139, Loss: 3.606, AUC: 0.913, AP: 0.909, Completeness: 0.488, Homogeneity: 0.500, NMI: 0.494\n",
      "Epoch: 140, Loss: 3.617, AUC: 0.913, AP: 0.910, Completeness: 0.490, Homogeneity: 0.502, NMI: 0.496\n",
      "Epoch: 141, Loss: 3.747, AUC: 0.913, AP: 0.910, Completeness: 0.482, Homogeneity: 0.496, NMI: 0.489\n",
      "Epoch: 142, Loss: 3.655, AUC: 0.913, AP: 0.910, Completeness: 0.484, Homogeneity: 0.498, NMI: 0.491\n",
      "Epoch: 143, Loss: 3.676, AUC: 0.913, AP: 0.910, Completeness: 0.483, Homogeneity: 0.497, NMI: 0.490\n",
      "Epoch: 144, Loss: 3.637, AUC: 0.913, AP: 0.909, Completeness: 0.483, Homogeneity: 0.497, NMI: 0.490\n",
      "Epoch: 145, Loss: 3.633, AUC: 0.913, AP: 0.908, Completeness: 0.473, Homogeneity: 0.487, NMI: 0.480\n",
      "Epoch: 146, Loss: 3.606, AUC: 0.913, AP: 0.907, Completeness: 0.470, Homogeneity: 0.486, NMI: 0.478\n",
      "Epoch: 147, Loss: 3.619, AUC: 0.912, AP: 0.906, Completeness: 0.469, Homogeneity: 0.484, NMI: 0.476\n",
      "Epoch: 148, Loss: 3.674, AUC: 0.912, AP: 0.905, Completeness: 0.470, Homogeneity: 0.485, NMI: 0.477\n",
      "Epoch: 149, Loss: 3.658, AUC: 0.911, AP: 0.905, Completeness: 0.470, Homogeneity: 0.485, NMI: 0.478\n",
      "Epoch: 150, Loss: 3.660, AUC: 0.911, AP: 0.904, Completeness: 0.447, Homogeneity: 0.452, NMI: 0.449\n",
      "Epoch: 151, Loss: 3.677, AUC: 0.911, AP: 0.904, Completeness: 0.468, Homogeneity: 0.483, NMI: 0.475\n",
      "Epoch: 152, Loss: 3.590, AUC: 0.911, AP: 0.904, Completeness: 0.466, Homogeneity: 0.482, NMI: 0.474\n",
      "Epoch: 153, Loss: 3.667, AUC: 0.911, AP: 0.905, Completeness: 0.428, Homogeneity: 0.438, NMI: 0.433\n",
      "Epoch: 154, Loss: 3.634, AUC: 0.911, AP: 0.906, Completeness: 0.428, Homogeneity: 0.437, NMI: 0.432\n",
      "Epoch: 155, Loss: 3.643, AUC: 0.911, AP: 0.906, Completeness: 0.426, Homogeneity: 0.436, NMI: 0.431\n",
      "Epoch: 156, Loss: 3.643, AUC: 0.911, AP: 0.906, Completeness: 0.422, Homogeneity: 0.432, NMI: 0.427\n",
      "Epoch: 157, Loss: 3.569, AUC: 0.911, AP: 0.907, Completeness: 0.450, Homogeneity: 0.465, NMI: 0.457\n",
      "Epoch: 158, Loss: 3.614, AUC: 0.911, AP: 0.908, Completeness: 0.443, Homogeneity: 0.457, NMI: 0.450\n",
      "Epoch: 159, Loss: 3.567, AUC: 0.911, AP: 0.908, Completeness: 0.441, Homogeneity: 0.455, NMI: 0.448\n",
      "Epoch: 160, Loss: 3.455, AUC: 0.911, AP: 0.909, Completeness: 0.438, Homogeneity: 0.452, NMI: 0.445\n",
      "Epoch: 161, Loss: 3.485, AUC: 0.912, AP: 0.909, Completeness: 0.436, Homogeneity: 0.449, NMI: 0.442\n",
      "Epoch: 162, Loss: 3.457, AUC: 0.912, AP: 0.909, Completeness: 0.397, Homogeneity: 0.411, NMI: 0.404\n",
      "Epoch: 163, Loss: 3.526, AUC: 0.912, AP: 0.909, Completeness: 0.440, Homogeneity: 0.453, NMI: 0.447\n",
      "Epoch: 164, Loss: 3.501, AUC: 0.911, AP: 0.910, Completeness: 0.441, Homogeneity: 0.454, NMI: 0.447\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 165, Loss: 3.475, AUC: 0.910, AP: 0.910, Completeness: 0.391, Homogeneity: 0.406, NMI: 0.399\n",
      "Epoch: 166, Loss: 3.490, AUC: 0.909, AP: 0.910, Completeness: 0.383, Homogeneity: 0.398, NMI: 0.391\n",
      "Epoch: 167, Loss: 3.477, AUC: 0.909, AP: 0.910, Completeness: 0.383, Homogeneity: 0.398, NMI: 0.390\n",
      "Epoch: 168, Loss: 3.524, AUC: 0.908, AP: 0.909, Completeness: 0.380, Homogeneity: 0.395, NMI: 0.387\n",
      "Epoch: 169, Loss: 3.537, AUC: 0.907, AP: 0.909, Completeness: 0.376, Homogeneity: 0.391, NMI: 0.384\n",
      "Epoch: 170, Loss: 3.493, AUC: 0.906, AP: 0.908, Completeness: 0.372, Homogeneity: 0.388, NMI: 0.380\n",
      "Epoch: 171, Loss: 3.520, AUC: 0.905, AP: 0.907, Completeness: 0.375, Homogeneity: 0.391, NMI: 0.383\n",
      "Epoch: 172, Loss: 3.581, AUC: 0.904, AP: 0.906, Completeness: 0.372, Homogeneity: 0.389, NMI: 0.380\n",
      "Epoch: 173, Loss: 3.584, AUC: 0.903, AP: 0.905, Completeness: 0.364, Homogeneity: 0.381, NMI: 0.372\n",
      "Epoch: 174, Loss: 3.544, AUC: 0.902, AP: 0.904, Completeness: 0.369, Homogeneity: 0.386, NMI: 0.377\n",
      "Epoch: 175, Loss: 3.546, AUC: 0.901, AP: 0.903, Completeness: 0.370, Homogeneity: 0.386, NMI: 0.378\n",
      "Epoch: 176, Loss: 3.532, AUC: 0.900, AP: 0.902, Completeness: 0.368, Homogeneity: 0.385, NMI: 0.376\n",
      "Epoch: 177, Loss: 3.598, AUC: 0.900, AP: 0.902, Completeness: 0.366, Homogeneity: 0.383, NMI: 0.374\n",
      "Epoch: 178, Loss: 3.514, AUC: 0.899, AP: 0.902, Completeness: 0.362, Homogeneity: 0.379, NMI: 0.370\n",
      "Epoch: 179, Loss: 3.441, AUC: 0.899, AP: 0.902, Completeness: 0.363, Homogeneity: 0.380, NMI: 0.372\n",
      "Epoch: 180, Loss: 3.470, AUC: 0.899, AP: 0.902, Completeness: 0.363, Homogeneity: 0.380, NMI: 0.371\n",
      "Epoch: 181, Loss: 3.479, AUC: 0.900, AP: 0.903, Completeness: 0.364, Homogeneity: 0.381, NMI: 0.372\n",
      "Epoch: 182, Loss: 3.460, AUC: 0.900, AP: 0.903, Completeness: 0.370, Homogeneity: 0.386, NMI: 0.378\n",
      "Epoch: 183, Loss: 3.483, AUC: 0.900, AP: 0.904, Completeness: 0.374, Homogeneity: 0.391, NMI: 0.382\n",
      "Epoch: 184, Loss: 3.435, AUC: 0.901, AP: 0.904, Completeness: 0.372, Homogeneity: 0.388, NMI: 0.380\n",
      "Epoch: 185, Loss: 3.476, AUC: 0.901, AP: 0.904, Completeness: 0.388, Homogeneity: 0.401, NMI: 0.395\n",
      "Epoch: 186, Loss: 3.484, AUC: 0.900, AP: 0.904, Completeness: 0.392, Homogeneity: 0.408, NMI: 0.400\n",
      "Epoch: 187, Loss: 3.519, AUC: 0.900, AP: 0.904, Completeness: 0.385, Homogeneity: 0.402, NMI: 0.393\n",
      "Epoch: 188, Loss: 3.450, AUC: 0.899, AP: 0.903, Completeness: 0.392, Homogeneity: 0.407, NMI: 0.399\n",
      "Epoch: 189, Loss: 3.425, AUC: 0.898, AP: 0.903, Completeness: 0.391, Homogeneity: 0.407, NMI: 0.399\n",
      "Epoch: 190, Loss: 3.461, AUC: 0.897, AP: 0.902, Completeness: 0.394, Homogeneity: 0.410, NMI: 0.402\n",
      "Epoch: 191, Loss: 3.499, AUC: 0.897, AP: 0.902, Completeness: 0.395, Homogeneity: 0.411, NMI: 0.403\n",
      "Epoch: 192, Loss: 3.401, AUC: 0.896, AP: 0.902, Completeness: 0.395, Homogeneity: 0.411, NMI: 0.403\n",
      "Epoch: 193, Loss: 3.523, AUC: 0.896, AP: 0.902, Completeness: 0.396, Homogeneity: 0.411, NMI: 0.403\n",
      "Epoch: 194, Loss: 3.433, AUC: 0.896, AP: 0.903, Completeness: 0.398, Homogeneity: 0.413, NMI: 0.406\n",
      "Epoch: 195, Loss: 3.448, AUC: 0.897, AP: 0.904, Completeness: 0.361, Homogeneity: 0.374, NMI: 0.367\n",
      "Epoch: 196, Loss: 3.404, AUC: 0.897, AP: 0.905, Completeness: 0.414, Homogeneity: 0.429, NMI: 0.422\n",
      "Epoch: 197, Loss: 3.416, AUC: 0.898, AP: 0.906, Completeness: 0.391, Homogeneity: 0.407, NMI: 0.399\n",
      "Epoch: 198, Loss: 3.408, AUC: 0.898, AP: 0.907, Completeness: 0.364, Homogeneity: 0.377, NMI: 0.370\n",
      "Epoch: 199, Loss: 3.487, AUC: 0.899, AP: 0.908, Completeness: 0.452, Homogeneity: 0.473, NMI: 0.462\n",
      "Epoch: 200, Loss: 3.412, AUC: 0.899, AP: 0.908, Completeness: 0.455, Homogeneity: 0.475, NMI: 0.465\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, 201):\n",
    "    loss = train()\n",
    "    auc, ap, completeness, hm, nmi = test()\n",
    "    print((f'Epoch: {epoch:03d}, Loss: {loss:.3f}, AUC: {auc:.3f}, '\n",
    "           f'AP: {ap:.3f}, Completeness: {completeness:.3f}, '\n",
    "           f'Homogeneity: {hm:.3f}, NMI: {nmi:.3f}'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "@torch.no_grad()\n",
    "def plot_points(colors):\n",
    "    model.eval()\n",
    "    z = model.encode(data.x, data.train_pos_edge_index)\n",
    "    z = TSNE(n_components=2).fit_transform(z.cpu().numpy())\n",
    "    y = data.y.cpu().numpy()\n",
    "\n",
    "    fig = plt.figure(1, figsize=(8, 8))\n",
    "    fig.clf()\n",
    "    for i in range(dataset.num_classes):\n",
    "        plt.scatter(z[y == i, 0], z[y == i, 1], s=20, color=colors[i])\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/antonio/anaconda3/envs/geometric_new/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:780: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
      "  warnings.warn(\n",
      "/home/antonio/anaconda3/envs/geometric_new/lib/python3.9/site-packages/sklearn/manifold/_t_sne.py:790: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%\n",
    "colors = [\n",
    "    '#ffc0cb', '#bada55', '#008080', '#420420', '#7fe5f0', '#065535', '#ffd700'\n",
    "]\n",
    "plot_points(colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
